Identifying candidates for targeted gait rehabilitation after stroke: better prediction through biomechanics-informed characterization

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Identifying candidates for targeted gait rehabilitation after stroke: better prediction through biomechanics-informed characterization

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Title: Identifying candidates for targeted gait rehabilitation after stroke: better prediction through biomechanics-informed characterization
Author: Awad, Louis N.; Reisman, Darcy S.; Pohlig, Ryan T.; Binder-Macleod, Stuart A.

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Citation: Awad, Louis N., Darcy S. Reisman, Ryan T. Pohlig, and Stuart A. Binder-Macleod. 2016. “Identifying candidates for targeted gait rehabilitation after stroke: better prediction through biomechanics-informed characterization.” Journal of NeuroEngineering and Rehabilitation 13 (1): 84. doi:10.1186/s12984-016-0188-8. http://dx.doi.org/10.1186/s12984-016-0188-8.
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Abstract: Background: Walking speed has been used to predict the efficacy of gait training; however, poststroke motor impairments are heterogeneous and different biomechanical strategies may underlie the same walking speed. Identifying which individuals will respond best to a particular gait rehabilitation program using walking speed alone may thus be limited. The objective of this study was to determine if, beyond walking speed, participants’ baseline ability to generate propulsive force from their paretic limbs (paretic propulsion) influences the improvements in walking speed resulting from a paretic propulsion-targeting gait intervention. Methods: Twenty seven participants >6 months poststroke underwent a 12-week locomotor training program designed to target deficits in paretic propulsion through the combination of fast walking with functional electrical stimulation to the paretic ankle musculature (FastFES). The relationship between participants’ baseline usual walking speed (UWSbaseline), maximum walking speed (MWSbaseline), and paretic propulsion (propbaseline) versus improvements in usual walking speed (∆UWS) and maximum walking speed (∆MWS) were evaluated in moderated regression models. Results: UWSbaseline and MWSbaseline were, respectively, poor predictors of ΔUWS (R2 = 0.24) and ΔMWS (R2 = 0.01). Paretic propulsion × walking speed interactions (UWSbaseline × propbaseline and MWSbaseline × propbaseline) were observed in each regression model (R2s = 0.61 and 0.49 for ∆UWS and ∆MWS, respectively), revealing that slower individuals with higher utilization of the paretic limb for forward propulsion responded best to FastFES training and were the most likely to achieve clinically important differences. Conclusions: Characterizing participants based on both their walking speed and ability to generate paretic propulsion is a markedly better approach to predicting walking recovery following targeted gait rehabilitation than using walking speed alone.
Published Version: doi:10.1186/s12984-016-0188-8
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5035477/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:29407839
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